Embodiments of the present invention are related to digital signal processing and, in particular, to dynamic range reduction of signals using histogram compaction.
In digital signal processing, a histogram can be used to display a visual representation of a digital signal. For example, in image processing an image histogram can show a tonal distribution of an image. Each bin in the histogram can represent a tone or range of tones and the height of each bar (which visually represents the value of the bin) can indicate how many pixels in the image have a tone corresponding to that bin. In video processing, each frame can be represented similarly using a histogram.
Dynamic range reduction can be used to serve a number of purposes. For example, the raw signal data received may be too large to be efficiently processed. Typical imaging systems may not have the resources or time to process 14˜16 bit image or video data. Additionally, the imaging system may not be equipped with a monitor capable of displaying 14˜16 bit data, and human eyes do not have gray scale resolution beyond 10 bits.
Prior art methods of reducing the dynamic range of a signal typically use a knee curve or smooth curves (such as logarithm/gamma curves) applied to the entire signal. This results in a number of shortcomings. In particular, the dynamic range reduction rate tends to be relatively limited. Additionally, prior art methods can result in noticeable saturation in the low and/or high ends of the signal. Quantization can also be observed where a large amount of the signal falls into the compacted portions of the signal. A loss of fidelity in the compacted portions of the signal is also observed. Further, the prior art methods may result in an unstable output signal, causing the observed output signal to fluctuate.
Embodiments of the invention address these and other problems.